A _rule_ in Kingfisher is a YAML document that describes how to detect and (optionally) validate secrets in your codebase. With custom rules you can:
- **Extend** Kingfisher without touching Rust code
- **Tune** sensitivity via entropy and confidence
- **Plug in** live checks against external services
This document explains how to write custom rules for Kingfisher using a YAML-based rule system. The rules define regular expressions to detect secrets in source code and other textual data, and they can include validation steps to confirm the secret's authenticity. By using a rules-based system, Kingfisher is highly extensible—new rules can be added or existing ones modified without changing the core code.
## 1. Rule Schema
Each rule file defines one or more entries under a top‑level `rules:` list. Every entry supports the following fields:
response_is_html: true # by default, validation responses containing HTML or considered invalid. Set to `true` if you expect HTML returned from a validation response
response_matcher:
- report_response: true # always include raw payload
Kingfisher leverages the Liquid template engine for dynamic parts of HTTP request bodies, headers, query parameters, and multipart payloads. The engine supports both built-in and custom filters to manipulate the captured secret (TOKEN) or other named captures ({{ NAME }}).
- **Capture Injection**: The unnamed capture from your regex becomes {{ TOKEN }}. Named captures are made available as uppercase variables (e.g. {{ RDMVAL }}).
- **Filter Pipeline**: You can chain filters using the pipe (|) syntax:
```liquid
{{ TOKEN | b64enc | url_encode }}
```
Arguments: Some filters accept parameters, provided after a colon:
```liquid
{{ TOKEN | hmac_sha256: "my-secret-key" }}
```
### 3. Built-in & Custom Liquid Filters
Below is the complete list of Liquid filters available in Kingfisher, along with their usage patterns and examples.
| `crc32` | – | Computes the CRC32 checksum of the input and returns a decimal value. | `{{ TOKEN \| crc32 }}` |
| `crc32_dec` | `digits` (integer, optional) | Computes the CRC32 checksum and returns the last `digits` decimal characters (zero-padded). Defaults to the full value when omitted. | `{{ TOKEN \| crc32_dec: 6 }}` |
| `crc32_hex` | `digits` (integer, optional) | Computes the CRC32 checksum and returns the last `digits` hexadecimal characters (zero-padded). Defaults to the full value when omitted. | `{{ TOKEN \| crc32_hex: 8 }}` |
| `crc32_le_b64` | `len` (integer, optional) | Computes the CRC32 checksum, encodes the little-endian bytes using Base64, and optionally truncates to the first `len` characters. | `{{ TOKEN \| crc32_le_b64: 6 }}` |
In your YAML rule definition, you add a `depends_on_rule` section. Here you specify:
- **rule_id:** The identifier of the rule whose output is required.
- **variable:** The name (typically in uppercase) that will be used to reference the captured value from the dependency rule.
- **Chaining Captures:**
When Kingfisher scans a file, it processes rules in a specific order. If a rule has a dependency, the engine first checks whether the dependent rule has already matched on the same input (or blob). If it did, the captured value (for example, an access key ID) is made available to the dependent rule.
- **Using the Captured Value:**
This captured value can then be used during the validation phase. For instance, if you have a rule for an Algolia Admin API Key that depends on an Algolia Application ID (captured as `APPID`), the validation logic can incorporate the `APPID` value to confirm that the secret matches the expected pattern or format for that specific account.
### Use depends_on_rule to require one rule before another runs:
```yaml
depends_on_rule:
- rule_id: kingfisher.algolia.app_id # must match first
variable: APPID # captured as {{ APPID }}
```
- **Capture flow**: First rule captures `APPID` → second rule injects `{{ APPID }}` into validation HTTP request or pattern
- **Visible control:** set `visible: false` on the supporting rule so it doesn’t clutter your report for non-secret matches
## Algolia Example
Consider this example rule for an Algolia Application ID and Admin Key combination. To validate that this is an active credential, both must be matched:
* Algolia Application ID Rule (kingfisher.algolia.2):
This rule scans for an Algolia Application ID—a 10-character alphanumeric string. It is marked with visible: false so that even if it matches, the finding is not directly reported. Its primary role is to provide a supporting value for other rules rather than to be flagged as a secret by itself.
* Algolia Admin API Key Rule (kingfisher.algolia.1):
This rule detects the Algolia Admin API Key using a regex pattern. It includes a depends_on_rule property that specifies a dependency on the Algolia Application ID rule.
* The dependency declares that the rule requires the output of the Algolia Application ID rule, and the captured value is assigned to the variable APPID.
* In the validation section, this captured `APPID` is used dynamically in the HTTP request (for example, in the header `X-Algolia-Application-Id` and in the URL).
The dependency mechanism (depends_on_rule) ensures that:
* Non-secret data (like an application ID) is captured without cluttering the scan report (thanks to visible: false).
* The secret (the API key) is validated in context, with the necessary supporting information automatically injected.
* Rules remain modular and extensible; you can update the dependent rule or its pattern independently, and the change will automatically be reflected where the value is used.
## The `visible: false` Property
The `visible: false` property tells Kingfisher to hide the finding from the final scan report. This is particularly useful for rules that capture data not meant to be reported as a secret, but rather to serve as supporting context for another rule.
For example, a rule might match a username, an email address, an AWS Access Key ID, or an Application ID. While these pieces of information are captured during scanning, they are not secrets on their own. Instead, they are used by other rules—via the `depends_on_rule` mechanism—to validate an associated secret. By marking such rules as `visible: false`, you prevent these non-secret findings from cluttering your report, yet their values remain available for dependent rules.
`visible: false` helps keep the scan output focused on actual secrets while still capturing important contextual data needed for comprehensive validation.
The `pattern_requirements` field allows you to specify data type requirements for matched secrets. This is particularly useful when:
- Your regex pattern must be permissive (due to Hyperscan limitations)
- You want to enforce password complexity requirements
- You need to filter out low-quality matches that lack certain character types
Kingfisher's regex engine (Hyperscan) does not support lookahead assertions like `(?=.*\d)` to require specific character types. Instead, use the `pattern_requirements` field to filter matches post-detection.
### Available Requirements
```yaml
pattern_requirements:
min_digits: 1 # Require at least 1 digit (0-9)
min_uppercase: 1 # Require at least 1 uppercase letter (A-Z)
min_lowercase: 1 # Require at least 1 lowercase letter (a-z)
min_special_chars: 1 # Require at least 1 special character
special_chars: "!@#$%^&*" # Optional: define which characters are "special"
`ignore_if_contains` performs a case-insensitive substring check. If any entry (after trimming whitespace) appears within the match, the match is discarded. This is helpful for dropping known dummy tokens such as "test" or "demo" that otherwise satisfy the regex.
The optional `checksum` block renders Liquid templates against the match to determine whether the captured checksum matches your expectation. Both templates gain access to `{{ MATCH }}`, `{{ FULL_MATCH }}`, and every named capture in two forms: the original capture name and its uppercase alias (e.g. `{{ body }}` and `{{ BODY }}`). Use helper filters like `suffix`, `crc32`, and `base62` to mirror provider-specific checksum pipelines. If a required capture is missing or the rendered values differ, Kingfisher skips the finding—logging the reason, including checksum lengths, at the `DEBUG` level. Set `skip_if_missing` to `true` to treat absent captures as legacy matches.
When any of these filters remove a match it is logged at the `DEBUG` level so you can see exactly why the skip occurred. If you need to keep every match even when one of these substrings appears, pass `--no-ignore-if-contains` to `kingfisher scan`. The flag disables this post-processing step without changing the rule definitions.
### Are `requires_capture` and `skip_if_missing` equivalent?
`requires_capture`
- Optional field that names a specific regex capture that must be present before the checksum templates are evaluated.
- In the engine, Kingfisher checks whether that capture exists in the match context. If it’s missing, the behavior falls back to whatever `skip_if_missing` dictates (fail or treat as a legacy match).
`skip_if_missing`
- Boolean switch that controls what happens when Kingfisher can’t render the checksum—because there’s no match context or a required capture is absent.
-`true`: silently skip (pass) the match so legacy, non-checksum tokens are still accepted.
-`false`: treat the situation as a validation failure.
In short, `requires_capture` identifies which capture must exist, while `skip_if_missing` determines whether missing data is a hard failure or an allowed legacy case.
When writing custom rules, consider the following best practices:
1.**Multi-line Regex:** Write your regex patterns over multiple lines for clarity. Use the `(?x)` flag to enable free-spacing mode.
2.**Optimize for Performance:** Structure your regex to minimize backtracking. Use non-capturing groups where possible and keep the pattern as concise as possible.
3.**Validation Integration:** Define a `validation` section if you want to verify the detected secret. You can use Liquid templating to insert dynamic values—use the unnamed capture as `TOKEN` and any named captures in uppercase.
4.**Test with Examples:** Always include examples that should match and, optionally, negative examples to ensure your rule behaves as expected.
## Examples
Below are some examples to guide you in writing custom rules
